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Author | Marta Diez-Ferrer; Debora Gil; Cristian Tebe; Carles Sanchez | ||||
Title | Positive Airway Pressure to Enhance Computed Tomography Imaging for Airway Segmentation for Virtual Bronchoscopic Navigation | Type | Journal Article | ||
Year | 2018 | Publication | Respiration | Abbreviated Journal | RES |
Volume | 96 | Issue | 6 | Pages | 525-534 |
Keywords | Multidetector computed tomography; Bronchoscopy; Continuous positive airway pressure; Image enhancement; Virtual bronchoscopic navigation | ||||
Abstract | Abstract
RATIONALE: Virtual bronchoscopic navigation (VBN) guidance to peripheral pulmonary lesions is often limited by insufficient segmentation of the peripheral airways. OBJECTIVES: To test the effect of applying positive airway pressure (PAP) during CT acquisition to improve segmentation, particularly at end-expiration. METHODS: CT acquisitions in inspiration and expiration with 4 PAP protocols were recorded prospectively and compared to baseline inspiratory acquisitions in 20 patients. The 4 protocols explored differences between devices (flow vs. turbine), exposures (within seconds vs. 15-min) and pressure levels (10 vs. 14 cmH2O). Segmentation quality was evaluated with the number of airways and number of endpoints reached. A generalized mixed-effects model explored the estimated effect of each protocol. MEASUREMENTS AND MAIN RESULTS: Patient characteristics and lung function did not significantly differ between protocols. Compared to baseline inspiratory acquisitions, expiratory acquisitions after 15 min of 14 cmH2O PAP segmented 1.63-fold more airways (95% CI 1.07-2.48; p = 0.018) and reached 1.34-fold more endpoints (95% CI 1.08-1.66; p = 0.004). Inspiratory acquisitions performed immediately under 10 cmH2O PAP reached 1.20-fold (95% CI 1.09-1.33; p < 0.001) more endpoints; after 15 min the increase was 1.14-fold (95% CI 1.05-1.24; p < 0.001). CONCLUSIONS: CT acquisitions with PAP segment more airways and reach more endpoints than baseline inspiratory acquisitions. The improvement is particularly evident at end-expiration after 15 min of 14 cmH2O PAP. Further studies must confirm that the improvement increases diagnostic yield when using VBN to evaluate peripheral pulmonary lesions. |
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Notes | IAM; 600.145 | Approved | no | ||
Call Number | Admin @ si @ DGT2018 | Serial | 3135 | ||
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Author | Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera | ||||
Title | Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification | Type | Journal Article | ||
Year | 2018 | Publication | Image and Vision Computing | Abbreviated Journal | IMAVIS |
Volume | 79 | Issue | Pages | 76-85 | |
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Abstract | Person re-identification has received special attention by the human analysis community in the last few years. To address the challenges in this field, many researchers have proposed different strategies, which basically exploit either cross-view invariant features or cross-view robust metrics. In this work, we propose to exploit a post-ranking approach and combine different feature representations through ranking aggregation. Spatial information, which potentially benefits the person matching, is represented using a 2D body model, from which color and texture information are extracted and combined. We also consider background/foreground information, automatically extracted via Deep Decompositional Network, and the usage of Convolutional Neural Network (CNN) features. To describe the matching between images we use the polynomial feature map, also taking into account local and global information. The Discriminant Context Information Analysis based post-ranking approach is used to improve initial ranking lists. Finally, the Stuart ranking aggregation method is employed to combine complementary ranking lists obtained from different feature representations. Experimental results demonstrated that we improve the state-of-the-art on VIPeR and PRID450s datasets, achieving 67.21% and 75.64% on top-1 rank recognition rate, respectively, as well as obtaining competitive results on CUHK01 dataset. | ||||
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Notes | HuPBA; 602.143 | Approved | no | ||
Call Number | Admin @ si @ JBE2018 | Serial | 3138 | ||
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Author | Boris N. Oreshkin; Pau Rodriguez; Alexandre Lacoste | ||||
Title | TADAM: Task dependent adaptive metric for improved few-shot learning | Type | Conference Article | ||
Year | 2018 | Publication | 32nd Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
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Abstract | Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space. Moreover, we propose and empirically test a practical end-to-end optimization procedure based on auxiliary task co-training to learn a task-dependent metric space. The resulting few-shot learning model based on the task-dependent scaled metric achieves state of the art on mini-Imagenet. We confirm these results on another few-shot dataset that we introduce in this paper based on CIFAR100. | ||||
Address | Montreal; Canada; December 2018 | ||||
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Area | Expedition | Conference | NIPS | ||
Notes | ISE; 600.098; 600.119 | Approved | no | ||
Call Number | Admin @ si @ ORL2018 | Serial | 3140 | ||
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Author | Mohammed Al Rawi; Dimosthenis Karatzas | ||||
Title | On the Labeling Correctness in Computer Vision Datasets | Type | Conference Article | ||
Year | 2018 | Publication | Proceedings of the Workshop on Interactive Adaptive Learning, co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases | Abbreviated Journal | |
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Abstract | Image datasets have heavily been used to build computer vision systems.
These datasets are either manually or automatically labeled, which is a problem as both labeling methods are prone to errors. To investigate this problem, we use a majority voting ensemble that combines the results from several Convolutional Neural Networks (CNNs). Majority voting ensembles not only enhance the overall performance, but can also be used to estimate the confidence level of each sample. We also examined Softmax as another form to estimate posterior probability. We have designed various experiments with a range of different ensembles built from one or different, or temporal/snapshot CNNs, which have been trained multiple times stochastically. We analyzed CIFAR10, CIFAR100, EMNIST, and SVHN datasets and we found quite a few incorrect labels, both in the training and testing sets. We also present detailed confidence analysis on these datasets and we found that the ensemble is better than the Softmax when used estimate the per-sample confidence. This work thus proposes an approach that can be used to scrutinize and verify the labeling of computer vision datasets, which can later be applied to weakly/semi-supervised learning. We propose a measure, based on the Odds-Ratio, to quantify how many of these incorrectly classified labels are actually incorrectly labeled and how many of these are confusing. The proposed methods are easily scalable to larger datasets, like ImageNet, LSUN and SUN, as each CNN instance is trained for 60 epochs; or even faster, by implementing a temporal (snapshot) ensemble. |
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Area | Expedition | Conference | ECML-PKDDW | ||
Notes | DAG; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ RaK2018 | Serial | 3144 | ||
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Author | Xavier Soria; Angel Sappa; Riad I. Hammoud | ||||
Title | Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images | Type | Journal Article | ||
Year | 2018 | Publication | Sensors | Abbreviated Journal | SENS |
Volume | 18 | Issue | 7 | Pages | 2059 |
Keywords | RGB-NIR sensor; multispectral imaging; deep learning; CNNs | ||||
Abstract | Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches. |
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Notes | ADAS; MSIAU; 600.086; 600.130; 600.122; 600.118 | Approved | no | ||
Call Number | Admin @ si @ SSH2018 | Serial | 3145 | ||
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Author | Adrian Galdran; Aitor Alvarez-Gila; Alessandro Bria; Javier Vazquez; Marcelo Bertalmio | ||||
Title | On the Duality Between Retinex and Image Dehazing | Type | Conference Article | ||
Year | 2018 | Publication | 31st IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 8212–8221 | ||
Keywords | Image color analysis; Task analysis; Atmospheric modeling; Computer vision; Computational modeling; Lighting | ||||
Abstract | Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different spectral lighting conditions. Retinex has been widely explored in the computer vision literature for image enhancement and other related tasks. While these two problems are apparently unrelated, the goal of this work is to show that they can be connected by a simple linear relationship. Specifically, most Retinex-based algorithms have the characteristic feature of always increasing image brightness, which turns them into ideal candidates for effective image dehazing by directly applying Retinex to a hazy image whose intensities have been inverted. In this paper, we give theoretical proof that Retinex on inverted intensities is a solution to the image dehazing problem. Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem. | ||||
Address | Salt Lake City; USA; June 2018 | ||||
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Area | Expedition | Conference | CVPR | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ GAB2018 | Serial | 3146 | ||
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Author | Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras | ||||
Title | Segmentation of aerial images for plausible detail synthesis | Type | Journal Article | ||
Year | 2018 | Publication | Computers & Graphics | Abbreviated Journal | CG |
Volume | 71 | Issue | Pages | 23-34 | |
Keywords | Terrain editing; Detail synthesis; Vegetation synthesis; Terrain rendering; Image segmentation | ||||
Abstract | The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts. | ||||
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ISSN | 0097-8493 | ISBN | Medium | ||
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Notes | MSIAU; 600.086; 600.118 | Approved | no | ||
Call Number | Admin @ si @ ACC2018 | Serial | 3147 | ||
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Author | Xim Cerda-Company; Xavier Otazu; Nilai Sallent; C. Alejandro Parraga | ||||
Title | The effect of luminance differences on color assimilation | Type | Journal Article | ||
Year | 2018 | Publication | Journal of Vision | Abbreviated Journal | JV |
Volume | 18 | Issue | 11 | Pages | 10-10 |
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Abstract | The color appearance of a surface depends on the color of its surroundings (inducers). When the perceived color shifts towards that of the surroundings, the effect is called “color assimilation” and when it shifts away from the surroundings it is called “color contrast.” There is also evidence that the phenomenon depends on the spatial configuration of the inducer, e.g., uniform surrounds tend to induce color contrast and striped surrounds tend to induce color assimilation. However, previous work found that striped surrounds under certain conditions do not induce color assimilation but induce color contrast (or do not induce anything at all), suggesting that luminance differences and high spatial frequencies could be key factors in color assimilation. Here we present a new psychophysical study of color assimilation where we assessed the contribution of luminance differences (between the target and its surround) present in striped stimuli. Our results show that luminance differences are key factors in color assimilation for stimuli varying along the s axis of MacLeod-Boynton color space, but not for stimuli varying along the l axis. This asymmetry suggests that koniocellular neural mechanisms responsible for color assimilation only contribute when there is a luminance difference, supporting the idea that mutual-inhibition has a major role in color induction. | ||||
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Notes | NEUROBIT; 600.120; 600.128 | Approved | no | ||
Call Number | Admin @ si @ COS2018 | Serial | 3148 | ||
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Author | Domicele Jonauskaite; Nele Dael; C. Alejandro Parraga; Laetitia Chevre; Alejandro Garcia Sanchez; Christine Mohr | ||||
Title | Stripping #The Dress: The importance of contextual information on inter-individual differences in colour perception | Type | Journal Article | ||
Year | 2018 | Publication | Psychological Research | Abbreviated Journal | PSYCHO R |
Volume | Issue | Pages | 1-15 | ||
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Abstract | In 2015, a picture of a Dress (henceforth the Dress) triggered popular and scientific interest; some reported seeing the Dress in white and gold (W&G) and others in blue and black (B&B). We aimed to describe the phenomenon and investigate the role of contextualization. Few days after the Dress had appeared on the Internet, we projected it to 240 students on two large screens in the classroom. Participants reported seeing the Dress in B&B (48%), W&G (38%), or blue and brown (B&Br; 7%). Amongst numerous socio-demographic variables, we only observed that W&G viewers were most likely to have always seen the Dress as W&G. In the laboratory, we tested how much contextual information is necessary for the phenomenon to occur. Fifty-seven participants selected colours most precisely matching predominant colours of parts or the full Dress. We presented, in this order, small squares (a), vertical strips (b), and the full Dress (c). We found that (1) B&B, B&Br, and W&G viewers had selected colours differing in lightness and chroma levels for contextualized images only (b, c conditions) and hue for fully contextualized condition only (c) and (2) B&B viewers selected colours most closely matching displayed colours of the Dress. Thus, the Dress phenomenon emerges due to inter-individual differences in subjectively perceived lightness, chroma, and hue, at least when all aspects of the picture need to be integrated. Our results support the previous conclusions that contextual information is key to colour perception; it should be important to understand how this actually happens. | ||||
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Notes | NEUROBIT; no proj | Approved | no | ||
Call Number | Admin @ si @ JDP2018 | Serial | 3149 | ||
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Author | Thanh Nam Le; Muhammad Muzzamil Luqman; Anjan Dutta; Pierre Heroux; Christophe Rigaud; Clement Guerin; Pasquale Foggia; Jean Christophe Burie; Jean Marc Ogier; Josep Llados; Sebastien Adam | ||||
Title | Subgraph spotting in graph representations of comic book images | Type | Journal Article | ||
Year | 2018 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 112 | Issue | Pages | 118-124 | |
Keywords | Attributed graph; Region adjacency graph; Graph matching; Graph isomorphism; Subgraph isomorphism; Subgraph spotting; Graph indexing; Graph retrieval; Query by example; Dataset and comic book images | ||||
Abstract | Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset. | ||||
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Notes | DAG; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ LLD2018 | Serial | 3150 | ||
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Author | Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados | ||||
Title | Aligning Salient Objects to Queries: A Multi-modal and Multi-object Image Retrieval Framework | Type | Conference Article | ||
Year | 2018 | Publication | 14th Asian Conference on Computer Vision | Abbreviated Journal | |
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Abstract | In this paper we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model learned from convolutional features. Both the alignment between the queries and the image and the supervision of the attention on the images are obtained by generalizing the Hungarian Algorithm using different loss functions. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set. We validate the performance of our approach on standard single/multi-object datasets, showing state-of-the art performance in every dataset. | ||||
Address | Perth; Australia; December 2018 | ||||
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Area | Expedition | Conference | ACCV | ||
Notes | DAG; 600.097; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ DDG2018a | Serial | 3151 | ||
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Author | Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados; Umapada Pal | ||||
Title | Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch | Type | Conference Article | ||
Year | 2018 | Publication | 24th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 916 - 921 | ||
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Abstract | In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query. A cross-modal deep network architecture is formulated to jointly model the sketch and text input modalities as well as the the image output modality, learning a common embedding between text and images and between sketches and images. In addition, an attention model is used to selectively focus the attention on the different objects of the image, allowing for retrieval with multiple objects in the query. Experiments show that the proposed method performs the best in both single and multiple object image retrieval in standard datasets. | ||||
Address | Beijing; China; August 2018 | ||||
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Area | Expedition | Conference | ICPR | ||
Notes | DAG; 602.167; 602.168; 600.097; 600.084; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ DDG2018b | Serial | 3152 | ||
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Author | Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce | ||||
Title | The Library Living Lab Barcelona: A participative approach to technology as an enabling factor for innovation in cultural spaces | Type | Journal | ||
Year | 2018 | Publication | Technology Innovation Management Review | Abbreviated Journal | |
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Notes | DAG; MV; 600.097; 600.121; 600.129;SIAI | Approved | no | ||
Call Number | Admin @ si @ VKV2018a | Serial | 3153 | ||
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Author | Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce | ||||
Title | Libraries as New Innovation Hubs: The Library Living Lab | Type | Conference Article | ||
Year | 2018 | Publication | 30th ISPIM Innovation Conference | Abbreviated Journal | |
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Abstract | Libraries are in deep transformation both in EU and around the world, and they are thriving within a great window of opportunity for innovation. In this paper, we show how the Library Living Lab in Barcelona participated of this changing scenario and contributed to create the Bibliolab program, where more than 200 public libraries give voice to their users in a global user-centric innovation initiative, using technology as enabling factor. The Library Living Lab is a real 4-helix implementation where Universities, Research Centers, Public Administration, Companies and the Neighbors are joint together to explore how technology transforms the cultural experience of people. This case is an example of scalability and provides reference tools for policy making, sustainability, user engage methodologies and governance. We provide specific examples of new prototypes and services that help to understand how to redefine the role of the Library as a real hub for social innovation. | ||||
Address | Stockholm; May 2018 | ||||
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Area | Expedition | Conference | ISPIM | ||
Notes | DAG; MV; 600.097; 600.121; 600.129;SIAI | Approved | no | ||
Call Number | Admin @ si @ VKV2018b | Serial | 3154 | ||
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Author | Abel Gonzalez-Garcia; Joost Van de Weijer; Yoshua Bengio | ||||
Title | Image-to-image translation for cross-domain disentanglement | Type | Conference Article | ||
Year | 2018 | Publication | 32nd Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
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Address | Montreal; Canada; December 2018 | ||||
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Area | Expedition | Conference | NIPS | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ GWB2018 | Serial | 3155 | ||
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